import concurrent.futures
import glob
import io
import os
import re
import sys
from contextlib import nullcontext
from pathlib import Path

import numpy as np
import pytest
import requests
import torch
import torchvision.transforms.v2.functional as F
from common_utils import assert_equal, cpu_and_cuda, IN_OSS_CI, needs_cuda
from PIL import __version__ as PILLOW_VERSION, Image, ImageOps, ImageSequence
from torchvision._internally_replaced_utils import IN_FBCODE
from torchvision.io.image import (
    _decode_avif,
    _decode_heic,
    decode_gif,
    decode_image,
    decode_jpeg,
    decode_png,
    decode_webp,
    encode_jpeg,
    encode_png,
    ImageReadMode,
    read_file,
    read_image,
    write_file,
    write_jpeg,
    write_png,
)

IMAGE_ROOT = os.path.join(os.path.dirname(os.path.abspath(__file__)), "assets")
FAKEDATA_DIR = os.path.join(IMAGE_ROOT, "fakedata")
IMAGE_DIR = os.path.join(FAKEDATA_DIR, "imagefolder")
DAMAGED_JPEG = os.path.join(IMAGE_ROOT, "damaged_jpeg")
DAMAGED_PNG = os.path.join(IMAGE_ROOT, "damaged_png")
ENCODE_JPEG = os.path.join(IMAGE_ROOT, "encode_jpeg")
INTERLACED_PNG = os.path.join(IMAGE_ROOT, "interlaced_png")
TOOSMALL_PNG = os.path.join(IMAGE_ROOT, "toosmall_png")
IS_WINDOWS = sys.platform in ("win32", "cygwin")
IS_MACOS = sys.platform == "darwin"
PILLOW_VERSION = tuple(int(x) for x in PILLOW_VERSION.split("."))
WEBP_TEST_IMAGES_DIR = os.environ.get("WEBP_TEST_IMAGES_DIR", "")

# Hacky way of figuring out whether we compiled with libavif/libheif (those are
# currenlty disabled by default)
try:
    _decode_avif(torch.arange(10, dtype=torch.uint8))
except Exception as e:
    DECODE_AVIF_ENABLED = "torchvision not compiled with libavif support" not in str(e)

try:
    _decode_heic(torch.arange(10, dtype=torch.uint8))
except Exception as e:
    DECODE_HEIC_ENABLED = "torchvision not compiled with libheif support" not in str(e)


def _get_safe_image_name(name):
    # Used when we need to change the pytest "id" for an "image path" parameter.
    # If we don't, the test id (i.e. its name) will contain the whole path to the image, which is machine-specific,
    # and this creates issues when the test is running in a different machine than where it was collected
    # (typically, in fb internal infra)
    return name.split(os.path.sep)[-1]


def get_images(directory, img_ext):
    assert os.path.isdir(directory)
    image_paths = glob.glob(directory + f"/**/*{img_ext}", recursive=True)
    for path in image_paths:
        if path.split(os.sep)[-2] not in ["damaged_jpeg", "jpeg_write"]:
            yield path


def pil_read_image(img_path):
    with Image.open(img_path) as img:
        return torch.from_numpy(np.array(img))


def normalize_dimensions(img_pil):
    if len(img_pil.shape) == 3:
        img_pil = img_pil.permute(2, 0, 1)
    else:
        img_pil = img_pil.unsqueeze(0)
    return img_pil


@pytest.mark.parametrize(
    "img_path",
    [pytest.param(jpeg_path, id=_get_safe_image_name(jpeg_path)) for jpeg_path in get_images(IMAGE_ROOT, ".jpg")],
)
@pytest.mark.parametrize(
    "pil_mode, mode",
    [
        (None, ImageReadMode.UNCHANGED),
        ("L", ImageReadMode.GRAY),
        ("RGB", ImageReadMode.RGB),
    ],
)
@pytest.mark.parametrize("scripted", (False, True))
@pytest.mark.parametrize("decode_fun", (decode_jpeg, decode_image))
def test_decode_jpeg(img_path, pil_mode, mode, scripted, decode_fun):

    with Image.open(img_path) as img:
        is_cmyk = img.mode == "CMYK"
        if pil_mode is not None:
            img = img.convert(pil_mode)
        img_pil = torch.from_numpy(np.array(img))
        if is_cmyk and mode == ImageReadMode.UNCHANGED:
            # flip the colors to match libjpeg
            img_pil = 255 - img_pil

    img_pil = normalize_dimensions(img_pil)
    data = read_file(img_path)
    if scripted:
        decode_fun = torch.jit.script(decode_fun)
    img_ljpeg = decode_fun(data, mode=mode)

    # Permit a small variation on pixel values to account for implementation
    # differences between Pillow and LibJPEG.
    abs_mean_diff = (img_ljpeg.type(torch.float32) - img_pil).abs().mean().item()
    assert abs_mean_diff < 2


@pytest.mark.parametrize("codec", ["png", "jpeg"])
@pytest.mark.parametrize("orientation", [1, 2, 3, 4, 5, 6, 7, 8, 0])
def test_decode_with_exif_orientation(tmpdir, codec, orientation):
    fp = os.path.join(tmpdir, f"exif_oriented_{orientation}.{codec}")
    t = torch.randint(0, 256, size=(3, 256, 257), dtype=torch.uint8)
    im = F.to_pil_image(t)
    exif = im.getexif()
    exif[0x0112] = orientation  # set exif orientation
    im.save(fp, codec.upper(), exif=exif.tobytes())

    data = read_file(fp)
    output = decode_image(data, apply_exif_orientation=True)

    pimg = Image.open(fp)
    pimg = ImageOps.exif_transpose(pimg)

    expected = F.pil_to_tensor(pimg)
    torch.testing.assert_close(expected, output)


@pytest.mark.parametrize("size", [65533, 1, 7, 10, 23, 33])
def test_invalid_exif(tmpdir, size):
    # Inspired from a PIL test:
    # https://github.com/python-pillow/Pillow/blob/8f63748e50378424628155994efd7e0739a4d1d1/Tests/test_file_jpeg.py#L299
    fp = os.path.join(tmpdir, "invalid_exif.jpg")
    t = torch.randint(0, 256, size=(3, 256, 257), dtype=torch.uint8)
    im = F.to_pil_image(t)
    im.save(fp, "JPEG", exif=b"1" * size)

    data = read_file(fp)
    output = decode_image(data, apply_exif_orientation=True)

    pimg = Image.open(fp)
    pimg = ImageOps.exif_transpose(pimg)

    expected = F.pil_to_tensor(pimg)
    torch.testing.assert_close(expected, output)


def test_decode_bad_huffman_images():
    # sanity check: make sure we can decode the bad Huffman encoding
    bad_huff = read_file(os.path.join(DAMAGED_JPEG, "bad_huffman.jpg"))
    decode_jpeg(bad_huff)


@pytest.mark.parametrize(
    "img_path",
    [
        pytest.param(truncated_image, id=_get_safe_image_name(truncated_image))
        for truncated_image in glob.glob(os.path.join(DAMAGED_JPEG, "corrupt*.jpg"))
    ],
)
def test_damaged_corrupt_images(img_path):
    # Truncated images should raise an exception
    data = read_file(img_path)
    if "corrupt34" in img_path:
        match_message = "Image is incomplete or truncated"
    else:
        match_message = "Unsupported marker type"
    with pytest.raises(RuntimeError, match=match_message):
        decode_jpeg(data)


@pytest.mark.parametrize(
    "img_path",
    [pytest.param(png_path, id=_get_safe_image_name(png_path)) for png_path in get_images(FAKEDATA_DIR, ".png")],
)
@pytest.mark.parametrize(
    "pil_mode, mode",
    [
        (None, ImageReadMode.UNCHANGED),
        ("L", ImageReadMode.GRAY),
        ("LA", ImageReadMode.GRAY_ALPHA),
        ("RGB", ImageReadMode.RGB),
        ("RGBA", ImageReadMode.RGB_ALPHA),
    ],
)
@pytest.mark.parametrize("scripted", (False, True))
@pytest.mark.parametrize("decode_fun", (decode_png, decode_image))
def test_decode_png(img_path, pil_mode, mode, scripted, decode_fun):

    if scripted:
        decode_fun = torch.jit.script(decode_fun)

    with Image.open(img_path) as img:
        if pil_mode is not None:
            img = img.convert(pil_mode)
        img_pil = torch.from_numpy(np.array(img))

    img_pil = normalize_dimensions(img_pil)

    if img_path.endswith("16.png"):
        data = read_file(img_path)
        img_lpng = decode_fun(data, mode=mode)
        assert img_lpng.dtype == torch.uint16
        # PIL converts 16 bits pngs to uint8
        img_lpng = F.to_dtype(img_lpng, torch.uint8, scale=True)
    else:
        data = read_file(img_path)
        img_lpng = decode_fun(data, mode=mode)

    tol = 0 if pil_mode is None else 1

    if PILLOW_VERSION >= (8, 3) and pil_mode == "LA":
        # Avoid checking the transparency channel until
        # https://github.com/python-pillow/Pillow/issues/5593#issuecomment-878244910
        # is fixed.
        # TODO: remove once fix is released in PIL. Should be > 8.3.1.
        img_lpng, img_pil = img_lpng[0], img_pil[0]

    torch.testing.assert_close(img_lpng, img_pil, atol=tol, rtol=0)


def test_decode_png_errors():
    with pytest.raises(RuntimeError, match="Out of bound read in decode_png"):
        decode_png(read_file(os.path.join(DAMAGED_PNG, "sigsegv.png")))
    with pytest.raises(RuntimeError, match="Content is too small for png"):
        decode_png(read_file(os.path.join(TOOSMALL_PNG, "heapbof.png")))


@pytest.mark.parametrize(
    "img_path",
    [pytest.param(png_path, id=_get_safe_image_name(png_path)) for png_path in get_images(IMAGE_DIR, ".png")],
)
@pytest.mark.parametrize("scripted", (True, False))
def test_encode_png(img_path, scripted):
    pil_image = Image.open(img_path)
    img_pil = torch.from_numpy(np.array(pil_image))
    img_pil = img_pil.permute(2, 0, 1)
    encode = torch.jit.script(encode_png) if scripted else encode_png
    png_buf = encode(img_pil, compression_level=6)

    rec_img = Image.open(io.BytesIO(bytes(png_buf.tolist())))
    rec_img = torch.from_numpy(np.array(rec_img))
    rec_img = rec_img.permute(2, 0, 1)

    assert_equal(img_pil, rec_img)


def test_encode_png_errors():
    with pytest.raises(RuntimeError, match="Input tensor dtype should be uint8"):
        encode_png(torch.empty((3, 100, 100), dtype=torch.float32))

    with pytest.raises(RuntimeError, match="Compression level should be between 0 and 9"):
        encode_png(torch.empty((3, 100, 100), dtype=torch.uint8), compression_level=-1)

    with pytest.raises(RuntimeError, match="Compression level should be between 0 and 9"):
        encode_png(torch.empty((3, 100, 100), dtype=torch.uint8), compression_level=10)

    with pytest.raises(RuntimeError, match="The number of channels should be 1 or 3, got: 5"):
        encode_png(torch.empty((5, 100, 100), dtype=torch.uint8))


@pytest.mark.parametrize(
    "img_path",
    [pytest.param(png_path, id=_get_safe_image_name(png_path)) for png_path in get_images(IMAGE_DIR, ".png")],
)
@pytest.mark.parametrize("scripted", (True, False))
def test_write_png(img_path, tmpdir, scripted):
    pil_image = Image.open(img_path)
    img_pil = torch.from_numpy(np.array(pil_image))
    img_pil = img_pil.permute(2, 0, 1)

    filename, _ = os.path.splitext(os.path.basename(img_path))
    torch_png = os.path.join(tmpdir, f"{filename}_torch.png")
    write = torch.jit.script(write_png) if scripted else write_png
    write(img_pil, torch_png, compression_level=6)
    saved_image = torch.from_numpy(np.array(Image.open(torch_png)))
    saved_image = saved_image.permute(2, 0, 1)

    assert_equal(img_pil, saved_image)


def test_read_image():
    # Just testing torchcsript, the functionality is somewhat tested already in other tests.
    path = next(get_images(IMAGE_ROOT, ".jpg"))
    out = read_image(path)
    out_scripted = torch.jit.script(read_image)(path)
    torch.testing.assert_close(out, out_scripted, atol=0, rtol=0)


@pytest.mark.parametrize("scripted", (True, False))
def test_read_file(tmpdir, scripted):
    fname, content = "test1.bin", b"TorchVision\211\n"
    fpath = os.path.join(tmpdir, fname)
    with open(fpath, "wb") as f:
        f.write(content)

    fun = torch.jit.script(read_file) if scripted else read_file
    data = fun(fpath)
    expected = torch.tensor(list(content), dtype=torch.uint8)
    os.unlink(fpath)
    assert_equal(data, expected)

    with pytest.raises(RuntimeError, match="No such file or directory: 'tst'"):
        read_file("tst")


def test_read_file_non_ascii(tmpdir):
    fname, content = "日本語(Japanese).bin", b"TorchVision\211\n"
    fpath = os.path.join(tmpdir, fname)
    with open(fpath, "wb") as f:
        f.write(content)

    data = read_file(fpath)
    expected = torch.tensor(list(content), dtype=torch.uint8)
    os.unlink(fpath)
    assert_equal(data, expected)


@pytest.mark.parametrize("scripted", (True, False))
def test_write_file(tmpdir, scripted):
    fname, content = "test1.bin", b"TorchVision\211\n"
    fpath = os.path.join(tmpdir, fname)
    content_tensor = torch.tensor(list(content), dtype=torch.uint8)
    write = torch.jit.script(write_file) if scripted else write_file
    write(fpath, content_tensor)

    with open(fpath, "rb") as f:
        saved_content = f.read()
    os.unlink(fpath)
    assert content == saved_content


def test_write_file_non_ascii(tmpdir):
    fname, content = "日本語(Japanese).bin", b"TorchVision\211\n"
    fpath = os.path.join(tmpdir, fname)
    content_tensor = torch.tensor(list(content), dtype=torch.uint8)
    write_file(fpath, content_tensor)

    with open(fpath, "rb") as f:
        saved_content = f.read()
    os.unlink(fpath)
    assert content == saved_content


@pytest.mark.parametrize(
    "shape",
    [
        (27, 27),
        (60, 60),
        (105, 105),
    ],
)
def test_read_1_bit_png(shape, tmpdir):
    np_rng = np.random.RandomState(0)
    image_path = os.path.join(tmpdir, f"test_{shape}.png")
    pixels = np_rng.rand(*shape) > 0.5
    img = Image.fromarray(pixels)
    img.save(image_path)
    img1 = read_image(image_path)
    img2 = normalize_dimensions(torch.as_tensor(pixels * 255, dtype=torch.uint8))
    assert_equal(img1, img2)


@pytest.mark.parametrize(
    "shape",
    [
        (27, 27),
        (60, 60),
        (105, 105),
    ],
)
@pytest.mark.parametrize(
    "mode",
    [
        ImageReadMode.UNCHANGED,
        ImageReadMode.GRAY,
    ],
)
def test_read_1_bit_png_consistency(shape, mode, tmpdir):
    np_rng = np.random.RandomState(0)
    image_path = os.path.join(tmpdir, f"test_{shape}.png")
    pixels = np_rng.rand(*shape) > 0.5
    img = Image.fromarray(pixels)
    img.save(image_path)
    img1 = read_image(image_path, mode)
    img2 = read_image(image_path, mode)
    assert_equal(img1, img2)


def test_read_interlaced_png():
    imgs = list(get_images(INTERLACED_PNG, ".png"))
    with Image.open(imgs[0]) as im1, Image.open(imgs[1]) as im2:
        assert not (im1.info.get("interlace") is im2.info.get("interlace"))
    img1 = read_image(imgs[0])
    img2 = read_image(imgs[1])
    assert_equal(img1, img2)


@needs_cuda
@pytest.mark.parametrize("mode", [ImageReadMode.UNCHANGED, ImageReadMode.GRAY, ImageReadMode.RGB])
@pytest.mark.parametrize("scripted", (False, True))
def test_decode_jpegs_cuda(mode, scripted):
    encoded_images = []
    for jpeg_path in get_images(IMAGE_ROOT, ".jpg"):
        if "cmyk" in jpeg_path:
            continue
        encoded_image = read_file(jpeg_path)
        encoded_images.append(encoded_image)
    decoded_images_cpu = decode_jpeg(encoded_images, mode=mode)
    decode_fn = torch.jit.script(decode_jpeg) if scripted else decode_jpeg

    # test multithreaded decoding
    # in the current version we prevent this by using a lock but we still want to test it
    num_workers = 10

    with concurrent.futures.ThreadPoolExecutor(max_workers=num_workers) as executor:
        futures = [executor.submit(decode_fn, encoded_images, mode, "cuda") for _ in range(num_workers)]
    decoded_images_threaded = [future.result() for future in futures]
    assert len(decoded_images_threaded) == num_workers
    for decoded_images in decoded_images_threaded:
        assert len(decoded_images) == len(encoded_images)
        for decoded_image_cuda, decoded_image_cpu in zip(decoded_images, decoded_images_cpu):
            assert decoded_image_cuda.shape == decoded_image_cpu.shape
            assert decoded_image_cuda.dtype == decoded_image_cpu.dtype == torch.uint8
            assert (decoded_image_cuda.cpu().float() - decoded_image_cpu.cpu().float()).abs().mean() < 2


@needs_cuda
def test_decode_image_cuda_raises():
    data = torch.randint(0, 127, size=(255,), device="cuda", dtype=torch.uint8)
    with pytest.raises(RuntimeError):
        decode_image(data)


@needs_cuda
def test_decode_jpeg_cuda_device_param():
    path = next(path for path in get_images(IMAGE_ROOT, ".jpg") if "cmyk" not in path)
    data = read_file(path)
    current_device = torch.cuda.current_device()
    current_stream = torch.cuda.current_stream()
    num_devices = torch.cuda.device_count()
    devices = ["cuda", torch.device("cuda")] + [torch.device(f"cuda:{i}") for i in range(num_devices)]
    results = []
    for device in devices:
        results.append(decode_jpeg(data, device=device))
    assert len(results) == len(devices)
    for result in results:
        assert torch.all(result.cpu() == results[0].cpu())
    assert current_device == torch.cuda.current_device()
    assert current_stream == torch.cuda.current_stream()


@needs_cuda
def test_decode_jpeg_cuda_errors():
    data = read_file(next(get_images(IMAGE_ROOT, ".jpg")))
    with pytest.raises(RuntimeError, match="Expected a non empty 1-dimensional tensor"):
        decode_jpeg(data.reshape(-1, 1), device="cuda")
    with pytest.raises(ValueError, match="must be tensors"):
        decode_jpeg([1, 2, 3])
    with pytest.raises(ValueError, match="Input tensor must be a CPU tensor"):
        decode_jpeg(data.to("cuda"), device="cuda")
    with pytest.raises(RuntimeError, match="Expected a torch.uint8 tensor"):
        decode_jpeg(data.to(torch.float), device="cuda")
    with pytest.raises(RuntimeError, match="Expected the device parameter to be a cuda device"):
        torch.ops.image.decode_jpegs_cuda([data], ImageReadMode.UNCHANGED.value, "cpu")
    with pytest.raises(ValueError, match="Input tensor must be a CPU tensor"):
        decode_jpeg(
            torch.empty((100,), dtype=torch.uint8, device="cuda"),
        )
    with pytest.raises(ValueError, match="Input list must contain tensors on CPU"):
        decode_jpeg(
            [
                torch.empty((100,), dtype=torch.uint8, device="cuda"),
                torch.empty((100,), dtype=torch.uint8, device="cuda"),
            ]
        )

    with pytest.raises(ValueError, match="Input list must contain tensors on CPU"):
        decode_jpeg(
            [
                torch.empty((100,), dtype=torch.uint8, device="cuda"),
                torch.empty((100,), dtype=torch.uint8, device="cuda"),
            ],
            device="cuda",
        )

    with pytest.raises(ValueError, match="Input list must contain tensors on CPU"):
        decode_jpeg(
            [
                torch.empty((100,), dtype=torch.uint8, device="cpu"),
                torch.empty((100,), dtype=torch.uint8, device="cuda"),
            ],
            device="cuda",
        )

    with pytest.raises(RuntimeError, match="Expected a torch.uint8 tensor"):
        decode_jpeg(
            [
                torch.empty((100,), dtype=torch.uint8),
                torch.empty((100,), dtype=torch.float32),
            ],
            device="cuda",
        )

    with pytest.raises(RuntimeError, match="Expected a non empty 1-dimensional tensor"):
        decode_jpeg(
            [
                torch.empty((100,), dtype=torch.uint8),
                torch.empty((1, 100), dtype=torch.uint8),
            ],
            device="cuda",
        )

    with pytest.raises(RuntimeError, match="Error while decoding JPEG images"):
        decode_jpeg(
            [
                torch.empty((100,), dtype=torch.uint8),
                torch.empty((100,), dtype=torch.uint8),
            ],
            device="cuda",
        )

    with pytest.raises(ValueError, match="Input list must contain at least one element"):
        decode_jpeg([], device="cuda")


def test_encode_jpeg_errors():

    with pytest.raises(RuntimeError, match="Input tensor dtype should be uint8"):
        encode_jpeg(torch.empty((3, 100, 100), dtype=torch.float32))

    with pytest.raises(ValueError, match="Image quality should be a positive number between 1 and 100"):
        encode_jpeg(torch.empty((3, 100, 100), dtype=torch.uint8), quality=-1)

    with pytest.raises(ValueError, match="Image quality should be a positive number between 1 and 100"):
        encode_jpeg(torch.empty((3, 100, 100), dtype=torch.uint8), quality=101)

    with pytest.raises(RuntimeError, match="The number of channels should be 1 or 3, got: 5"):
        encode_jpeg(torch.empty((5, 100, 100), dtype=torch.uint8))

    with pytest.raises(RuntimeError, match="Input data should be a 3-dimensional tensor"):
        encode_jpeg(torch.empty((1, 3, 100, 100), dtype=torch.uint8))

    with pytest.raises(RuntimeError, match="Input data should be a 3-dimensional tensor"):
        encode_jpeg(torch.empty((100, 100), dtype=torch.uint8))


@pytest.mark.skipif(IS_MACOS, reason="https://github.com/pytorch/vision/issues/8031")
@pytest.mark.parametrize(
    "img_path",
    [pytest.param(jpeg_path, id=_get_safe_image_name(jpeg_path)) for jpeg_path in get_images(ENCODE_JPEG, ".jpg")],
)
@pytest.mark.parametrize("scripted", (True, False))
def test_encode_jpeg(img_path, scripted):
    img = read_image(img_path)

    pil_img = F.to_pil_image(img)
    buf = io.BytesIO()
    pil_img.save(buf, format="JPEG", quality=75)

    encoded_jpeg_pil = torch.frombuffer(buf.getvalue(), dtype=torch.uint8)

    encode = torch.jit.script(encode_jpeg) if scripted else encode_jpeg
    for src_img in [img, img.contiguous()]:
        encoded_jpeg_torch = encode(src_img, quality=75)
        assert_equal(encoded_jpeg_torch, encoded_jpeg_pil)


@needs_cuda
def test_encode_jpeg_cuda_device_param():
    path = next(path for path in get_images(IMAGE_ROOT, ".jpg") if "cmyk" not in path)

    data = read_image(path)

    current_device = torch.cuda.current_device()
    current_stream = torch.cuda.current_stream()
    num_devices = torch.cuda.device_count()
    devices = ["cuda", torch.device("cuda")] + [torch.device(f"cuda:{i}") for i in range(num_devices)]
    results = []
    for device in devices:
        results.append(encode_jpeg(data.to(device=device)))
    assert len(results) == len(devices)
    for result in results:
        assert torch.all(result.cpu() == results[0].cpu())
    assert current_device == torch.cuda.current_device()
    assert current_stream == torch.cuda.current_stream()


@needs_cuda
@pytest.mark.parametrize(
    "img_path",
    [pytest.param(jpeg_path, id=_get_safe_image_name(jpeg_path)) for jpeg_path in get_images(IMAGE_ROOT, ".jpg")],
)
@pytest.mark.parametrize("scripted", (False, True))
@pytest.mark.parametrize("contiguous", (False, True))
def test_encode_jpeg_cuda(img_path, scripted, contiguous):
    decoded_image_tv = read_image(img_path)
    encode_fn = torch.jit.script(encode_jpeg) if scripted else encode_jpeg

    if "cmyk" in img_path:
        pytest.xfail("Encoding a CMYK jpeg isn't supported")
    if decoded_image_tv.shape[0] == 1:
        pytest.xfail("Decoding a grayscale jpeg isn't supported")
        # For more detail as to why check out: https://github.com/NVIDIA/cuda-samples/issues/23#issuecomment-559283013
    if contiguous:
        decoded_image_tv = decoded_image_tv[None].contiguous(memory_format=torch.contiguous_format)[0]
    else:
        decoded_image_tv = decoded_image_tv[None].contiguous(memory_format=torch.channels_last)[0]
    encoded_jpeg_cuda_tv = encode_fn(decoded_image_tv.cuda(), quality=75)
    decoded_jpeg_cuda_tv = decode_jpeg(encoded_jpeg_cuda_tv.cpu())

    # the actual encoded bytestreams from libnvjpeg and libjpeg-turbo differ for the same quality
    # instead, we re-decode the encoded image and compare to the original
    abs_mean_diff = (decoded_jpeg_cuda_tv.float() - decoded_image_tv.float()).abs().mean().item()
    assert abs_mean_diff < 3


@pytest.mark.parametrize("device", cpu_and_cuda())
@pytest.mark.parametrize("scripted", (True, False))
@pytest.mark.parametrize("contiguous", (True, False))
def test_encode_jpegs_batch(scripted, contiguous, device):
    if device == "cpu" and IS_MACOS:
        pytest.skip("https://github.com/pytorch/vision/issues/8031")
    decoded_images_tv = []
    for jpeg_path in get_images(IMAGE_ROOT, ".jpg"):
        if "cmyk" in jpeg_path:
            continue
        decoded_image = read_image(jpeg_path)
        if decoded_image.shape[0] == 1:
            continue
        if contiguous:
            decoded_image = decoded_image[None].contiguous(memory_format=torch.contiguous_format)[0]
        else:
            decoded_image = decoded_image[None].contiguous(memory_format=torch.channels_last)[0]
        decoded_images_tv.append(decoded_image)

    encode_fn = torch.jit.script(encode_jpeg) if scripted else encode_jpeg

    decoded_images_tv_device = [img.to(device=device) for img in decoded_images_tv]
    encoded_jpegs_tv_device = encode_fn(decoded_images_tv_device, quality=75)
    encoded_jpegs_tv_device = [decode_jpeg(img.cpu()) for img in encoded_jpegs_tv_device]

    for original, encoded_decoded in zip(decoded_images_tv, encoded_jpegs_tv_device):
        c, h, w = original.shape
        abs_mean_diff = (original.float() - encoded_decoded.float()).abs().mean().item()
        assert abs_mean_diff < 3

    # test multithreaded decoding
    # in the current version we prevent this by using a lock but we still want to test it
    num_workers = 10
    with concurrent.futures.ThreadPoolExecutor(max_workers=num_workers) as executor:
        futures = [executor.submit(encode_fn, decoded_images_tv_device) for _ in range(num_workers)]
    encoded_images_threaded = [future.result() for future in futures]
    assert len(encoded_images_threaded) == num_workers
    for encoded_images in encoded_images_threaded:
        assert len(decoded_images_tv_device) == len(encoded_images)
        for i, (encoded_image_cuda, decoded_image_tv) in enumerate(zip(encoded_images, decoded_images_tv_device)):
            # make sure all the threads produce identical outputs
            assert torch.all(encoded_image_cuda == encoded_images_threaded[0][i])

            # make sure the outputs are identical or close enough to baseline
            decoded_cuda_encoded_image = decode_jpeg(encoded_image_cuda.cpu())
            assert decoded_cuda_encoded_image.shape == decoded_image_tv.shape
            assert decoded_cuda_encoded_image.dtype == decoded_image_tv.dtype
            assert (decoded_cuda_encoded_image.cpu().float() - decoded_image_tv.cpu().float()).abs().mean() < 3


@needs_cuda
def test_single_encode_jpeg_cuda_errors():
    with pytest.raises(RuntimeError, match="Input tensor dtype should be uint8"):
        encode_jpeg(torch.empty((3, 100, 100), dtype=torch.float32, device="cuda"))

    with pytest.raises(RuntimeError, match="The number of channels should be 3, got: 5"):
        encode_jpeg(torch.empty((5, 100, 100), dtype=torch.uint8, device="cuda"))

    with pytest.raises(RuntimeError, match="The number of channels should be 3, got: 1"):
        encode_jpeg(torch.empty((1, 100, 100), dtype=torch.uint8, device="cuda"))

    with pytest.raises(RuntimeError, match="Input data should be a 3-dimensional tensor"):
        encode_jpeg(torch.empty((1, 3, 100, 100), dtype=torch.uint8, device="cuda"))

    with pytest.raises(RuntimeError, match="Input data should be a 3-dimensional tensor"):
        encode_jpeg(torch.empty((100, 100), dtype=torch.uint8, device="cuda"))


@needs_cuda
def test_batch_encode_jpegs_cuda_errors():
    with pytest.raises(RuntimeError, match="Input tensor dtype should be uint8"):
        encode_jpeg(
            [
                torch.empty((3, 100, 100), dtype=torch.uint8, device="cuda"),
                torch.empty((3, 100, 100), dtype=torch.float32, device="cuda"),
            ]
        )

    with pytest.raises(RuntimeError, match="The number of channels should be 3, got: 5"):
        encode_jpeg(
            [
                torch.empty((3, 100, 100), dtype=torch.uint8, device="cuda"),
                torch.empty((5, 100, 100), dtype=torch.uint8, device="cuda"),
            ]
        )

    with pytest.raises(RuntimeError, match="The number of channels should be 3, got: 1"):
        encode_jpeg(
            [
                torch.empty((3, 100, 100), dtype=torch.uint8, device="cuda"),
                torch.empty((1, 100, 100), dtype=torch.uint8, device="cuda"),
            ]
        )

    with pytest.raises(RuntimeError, match="Input data should be a 3-dimensional tensor"):
        encode_jpeg(
            [
                torch.empty((3, 100, 100), dtype=torch.uint8, device="cuda"),
                torch.empty((1, 3, 100, 100), dtype=torch.uint8, device="cuda"),
            ]
        )

    with pytest.raises(RuntimeError, match="Input data should be a 3-dimensional tensor"):
        encode_jpeg(
            [
                torch.empty((3, 100, 100), dtype=torch.uint8, device="cuda"),
                torch.empty((100, 100), dtype=torch.uint8, device="cuda"),
            ]
        )

    with pytest.raises(RuntimeError, match="Input tensor should be on CPU"):
        encode_jpeg(
            [
                torch.empty((3, 100, 100), dtype=torch.uint8, device="cpu"),
                torch.empty((3, 100, 100), dtype=torch.uint8, device="cuda"),
            ]
        )

    with pytest.raises(
        RuntimeError, match="All input tensors must be on the same CUDA device when encoding with nvjpeg"
    ):
        encode_jpeg(
            [
                torch.empty((3, 100, 100), dtype=torch.uint8, device="cuda"),
                torch.empty((3, 100, 100), dtype=torch.uint8, device="cpu"),
            ]
        )

    if torch.cuda.device_count() >= 2:
        with pytest.raises(
            RuntimeError, match="All input tensors must be on the same CUDA device when encoding with nvjpeg"
        ):
            encode_jpeg(
                [
                    torch.empty((3, 100, 100), dtype=torch.uint8, device="cuda:0"),
                    torch.empty((3, 100, 100), dtype=torch.uint8, device="cuda:1"),
                ]
            )

    with pytest.raises(ValueError, match="encode_jpeg requires at least one input tensor when a list is passed"):
        encode_jpeg([])


@pytest.mark.skipif(IS_MACOS, reason="https://github.com/pytorch/vision/issues/8031")
@pytest.mark.parametrize(
    "img_path",
    [pytest.param(jpeg_path, id=_get_safe_image_name(jpeg_path)) for jpeg_path in get_images(ENCODE_JPEG, ".jpg")],
)
@pytest.mark.parametrize("scripted", (True, False))
def test_write_jpeg(img_path, tmpdir, scripted):
    tmpdir = Path(tmpdir)
    img = read_image(img_path)
    pil_img = F.to_pil_image(img)

    torch_jpeg = str(tmpdir / "torch.jpg")
    pil_jpeg = str(tmpdir / "pil.jpg")

    write = torch.jit.script(write_jpeg) if scripted else write_jpeg
    write(img, torch_jpeg, quality=75)
    pil_img.save(pil_jpeg, quality=75)

    with open(torch_jpeg, "rb") as f:
        torch_bytes = f.read()

    with open(pil_jpeg, "rb") as f:
        pil_bytes = f.read()

    assert_equal(torch_bytes, pil_bytes)


def test_pathlib_support(tmpdir):
    # Just make sure pathlib.Path is supported where relevant

    jpeg_path = Path(next(get_images(ENCODE_JPEG, ".jpg")))

    read_file(jpeg_path)
    read_image(jpeg_path)

    write_path = Path(tmpdir) / "whatever"
    img = torch.randint(0, 10, size=(3, 4, 4), dtype=torch.uint8)

    write_file(write_path, data=img.flatten())
    write_jpeg(img, write_path)
    write_png(img, write_path)


@pytest.mark.parametrize(
    "name", ("gifgrid", "fire", "porsche", "treescap", "treescap-interlaced", "solid2", "x-trans", "earth")
)
@pytest.mark.parametrize("scripted", (True, False))
def test_decode_gif(tmpdir, name, scripted):
    # Using test images from GIFLIB
    # https://sourceforge.net/p/giflib/code/ci/master/tree/pic/, we assert PIL
    # and torchvision decoded outputs are equal.
    # We're not testing against "welcome2" because PIL and GIFLIB disagee on what
    # the background color should be (likely a difference in the way they handle
    # transparency?)
    # 'earth' image is from wikipedia, licensed under CC BY-SA 3.0
    # https://creativecommons.org/licenses/by-sa/3.0/
    # it allows to properly test for transparency, TOP-LEFT offsets, and
    # disposal modes.

    path = tmpdir / f"{name}.gif"
    if name == "earth":
        if IN_OSS_CI:
            # TODO: Fix this... one day.
            pytest.skip("Skipping 'earth' test as it's flaky on OSS CI")
        url = "https://upload.wikimedia.org/wikipedia/commons/2/2c/Rotating_earth_%28large%29.gif"
    else:
        url = f"https://sourceforge.net/p/giflib/code/ci/master/tree/pic/{name}.gif?format=raw"
    with open(path, "wb") as f:
        f.write(requests.get(url).content)

    encoded_bytes = read_file(path)
    f = torch.jit.script(decode_gif) if scripted else decode_gif
    tv_out = f(encoded_bytes)
    if tv_out.ndim == 3:
        tv_out = tv_out[None]

    assert tv_out.is_contiguous(memory_format=torch.channels_last)

    # For some reason, not using Image.open() as a CM causes "ResourceWarning: unclosed file"
    with Image.open(path) as pil_img:
        pil_seq = ImageSequence.Iterator(pil_img)

        for pil_frame, tv_frame in zip(pil_seq, tv_out):
            pil_frame = F.pil_to_tensor(pil_frame.convert("RGB"))
            torch.testing.assert_close(tv_frame, pil_frame, atol=0, rtol=0)


decode_fun_and_match = [
    (decode_png, "Content is not png"),
    (decode_jpeg, "Not a JPEG file"),
    (decode_gif, re.escape("DGifOpenFileName() failed - 103")),
    (decode_webp, "WebPGetFeatures failed."),
]
if DECODE_AVIF_ENABLED:
    decode_fun_and_match.append((_decode_avif, "BMFF parsing failed"))
if DECODE_HEIC_ENABLED:
    decode_fun_and_match.append((_decode_heic, "Invalid input: No 'ftyp' box"))


@pytest.mark.parametrize("decode_fun, match", decode_fun_and_match)
def test_decode_bad_encoded_data(decode_fun, match):
    encoded_data = torch.randint(0, 256, (100,), dtype=torch.uint8)
    with pytest.raises(RuntimeError, match="Input tensor must be 1-dimensional"):
        decode_fun(encoded_data[None])
    with pytest.raises(RuntimeError, match="Input tensor must have uint8 data type"):
        decode_fun(encoded_data.float())
    with pytest.raises(RuntimeError, match="Input tensor must be contiguous"):
        decode_fun(encoded_data[::2])
    with pytest.raises(RuntimeError, match=match):
        decode_fun(encoded_data)


@pytest.mark.parametrize("decode_fun", (decode_webp, decode_image))
@pytest.mark.parametrize("scripted", (False, True))
def test_decode_webp(decode_fun, scripted):
    encoded_bytes = read_file(next(get_images(FAKEDATA_DIR, ".webp")))
    if scripted:
        decode_fun = torch.jit.script(decode_fun)
    img = decode_fun(encoded_bytes)
    assert img.shape == (3, 100, 100)
    assert img[None].is_contiguous(memory_format=torch.channels_last)
    img += 123  # make sure image buffer wasn't freed by underlying decoding lib


# This test is skipped by default because it requires webp images that we're not
# including within the repo. The test images were downloaded manually from the
# different pages of https://developers.google.com/speed/webp/gallery
@pytest.mark.skipif(not WEBP_TEST_IMAGES_DIR, reason="WEBP_TEST_IMAGES_DIR is not set")
@pytest.mark.parametrize("decode_fun", (decode_webp, decode_image))
@pytest.mark.parametrize("scripted", (False, True))
@pytest.mark.parametrize(
    "mode, pil_mode",
    (
        # Note that converting an RGBA image to RGB leads to bad results because the
        # transparent pixels aren't necessarily set to "black" or "white", they can be
        # random stuff. This is consistent with PIL results.
        (ImageReadMode.RGB, "RGB"),
        (ImageReadMode.RGB_ALPHA, "RGBA"),
        (ImageReadMode.UNCHANGED, None),
    ),
)
@pytest.mark.parametrize("filename", Path(WEBP_TEST_IMAGES_DIR).glob("*.webp"), ids=lambda p: p.name)
def test_decode_webp_against_pil(decode_fun, scripted, mode, pil_mode, filename):
    encoded_bytes = read_file(filename)
    if scripted:
        decode_fun = torch.jit.script(decode_fun)
    img = decode_fun(encoded_bytes, mode=mode)
    assert img[None].is_contiguous(memory_format=torch.channels_last)

    pil_img = Image.open(filename).convert(pil_mode)
    from_pil = F.pil_to_tensor(pil_img)
    assert_equal(img, from_pil)
    img += 123  # make sure image buffer wasn't freed by underlying decoding lib


@pytest.mark.skipif(not DECODE_AVIF_ENABLED, reason="AVIF support not enabled.")
@pytest.mark.parametrize("decode_fun", (_decode_avif, decode_image))
@pytest.mark.parametrize("scripted", (False, True))
def test_decode_avif(decode_fun, scripted):
    encoded_bytes = read_file(next(get_images(FAKEDATA_DIR, ".avif")))
    if scripted:
        decode_fun = torch.jit.script(decode_fun)
    img = decode_fun(encoded_bytes)
    assert img.shape == (3, 100, 100)
    assert img[None].is_contiguous(memory_format=torch.channels_last)
    img += 123  # make sure image buffer wasn't freed by underlying decoding lib


# Note: decode_image fails because some of these files have a (valid) signature
# we don't recognize. We should probably use libmagic....
decode_funs = []
if DECODE_AVIF_ENABLED:
    decode_funs.append(_decode_avif)
if DECODE_HEIC_ENABLED:
    decode_funs.append(_decode_heic)


@pytest.mark.skipif(not decode_funs, reason="Built without avif and heic support.")
@pytest.mark.parametrize("decode_fun", decode_funs)
@pytest.mark.parametrize("scripted", (False, True))
@pytest.mark.parametrize(
    "mode, pil_mode",
    (
        (ImageReadMode.RGB, "RGB"),
        (ImageReadMode.RGB_ALPHA, "RGBA"),
        (ImageReadMode.UNCHANGED, None),
    ),
)
@pytest.mark.parametrize(
    "filename", Path("/home/nicolashug/dev/libavif/tests/data/").glob("*.avif"), ids=lambda p: p.name
)
def test_decode_avif_heic_against_pil(decode_fun, scripted, mode, pil_mode, filename):
    if "reversed_dimg_order" in str(filename):
        # Pillow properly decodes this one, but we don't (order of parts of the
        # image is wrong). This is due to a bug that was recently fixed in
        # libavif. Hopefully this test will end up passing soon with a new
        # libavif version https://github.com/AOMediaCodec/libavif/issues/2311
        pytest.xfail()
    import pillow_avif  # noqa

    encoded_bytes = read_file(filename)
    if scripted:
        decode_fun = torch.jit.script(decode_fun)
    try:
        img = decode_fun(encoded_bytes, mode=mode)
    except RuntimeError as e:
        if any(
            s in str(e)
            for s in (
                "BMFF parsing failed",
                "avifDecoderParse failed: ",
                "file contains more than one image",
                "no 'ispe' property",
                "'iref' has double references",
                "Invalid image grid",
            )
        ):
            pytest.skip(reason="Expected failure, that's OK")
        else:
            raise e
    assert img[None].is_contiguous(memory_format=torch.channels_last)
    if mode == ImageReadMode.RGB:
        assert img.shape[0] == 3
    if mode == ImageReadMode.RGB_ALPHA:
        assert img.shape[0] == 4

    if img.dtype == torch.uint16:
        img = F.to_dtype(img, dtype=torch.uint8, scale=True)
    try:
        from_pil = F.pil_to_tensor(Image.open(filename).convert(pil_mode))
    except RuntimeError as e:
        if "Invalid image grid" in str(e):
            pytest.skip(reason="PIL failure")
        else:
            raise e

    if True:
        from torchvision.utils import make_grid

        g = make_grid([img, from_pil])
        F.to_pil_image(g).save((f"/home/nicolashug/out_images/{filename.name}.{pil_mode}.png"))

    is_decode_heic = getattr(decode_fun, "__name__", getattr(decode_fun, "name", None)) == "_decode_heic"
    if mode == ImageReadMode.RGB and not is_decode_heic:
        # We don't compare torchvision's AVIF against PIL for RGB because
        # results look pretty different on RGBA images (other images are fine).
        # The result on torchvision basically just plainly ignores the alpha
        # channel, resuting in transparent pixels looking dark. PIL seems to be
        # using a sort of k-nn thing (Take a look at the resuting images)
        return
    if filename.name == "sofa_grid1x5_420.avif" and is_decode_heic:
        return

    torch.testing.assert_close(img, from_pil, rtol=0, atol=3)


@pytest.mark.skipif(not DECODE_HEIC_ENABLED, reason="HEIC support not enabled yet.")
@pytest.mark.parametrize("decode_fun", (_decode_heic, decode_image))
@pytest.mark.parametrize("scripted", (False, True))
def test_decode_heic(decode_fun, scripted):
    encoded_bytes = read_file(next(get_images(FAKEDATA_DIR, ".heic")))
    if scripted:
        decode_fun = torch.jit.script(decode_fun)
    img = decode_fun(encoded_bytes)
    assert img.shape == (3, 100, 100)
    assert img[None].is_contiguous(memory_format=torch.channels_last)
    img += 123  # make sure image buffer wasn't freed by underlying decoding lib


@pytest.mark.parametrize("input_type", ("Path", "str", "tensor"))
@pytest.mark.parametrize("scripted", (False, True))
def test_decode_image_path(input_type, scripted):
    # Check that decode_image can support not just tensors as input
    path = next(get_images(IMAGE_ROOT, ".jpg"))
    if input_type == "Path":
        input = Path(path)
    elif input_type == "str":
        input = path
    elif input_type == "tensor":
        input = read_file(path)
    else:
        raise ValueError("Oops")

    if scripted and input_type == "Path":
        pytest.xfail(reason="Can't pass a Path when scripting")

    decode_fun = torch.jit.script(decode_image) if scripted else decode_image
    decode_fun(input)


def test_mode_str():
    # Make sure decode_image supports string modes. We just test decode_image,
    # not all of the decoding functions, but they should all support that too.
    # Torchscript fails when passing strings, which is expected.
    path = next(get_images(IMAGE_ROOT, ".png"))
    assert decode_image(path, mode="RGB").shape[0] == 3
    assert decode_image(path, mode="rGb").shape[0] == 3
    assert decode_image(path, mode="GRAY").shape[0] == 1
    assert decode_image(path, mode="RGBA").shape[0] == 4


def test_avif_heic_fbcode():
    cm = nullcontext() if IN_FBCODE else pytest.raises(ImportError, match="cannot import")
    with cm:
        from torchvision.io import decode_heic  # noqa
    with cm:
        from torchvision.io import decode_avif  # noqa


if __name__ == "__main__":
    pytest.main([__file__])
